Transfer of Skills Learned on a Driving Simulator to On-Road Driving Behavior
Bibliographic record
Abstract
A long-term, naturalistic, prospective-cohort transfer of training study was conducted in commercial driving schools in Quebec, Canada, to test the effects on driver performance and behavior of integrating driving simulator–based training (DSBT) into the driver education program. For the study, 1 h of DSBT could be substituted for 1 h of on-road training for up to six of the mandatory 15 h. Four driving schools provided a convenience sample of 1,120 learner drivers (average age 17.7 and 52.7% female) between January 2010 and December 2014. Of the study sample, 95% received 1 to 4 h of DSBT. Those in the comparison group were all new, young Quebec drivers who had completed the mandatory driver education program in the same period. This paper reports on the association between DSBT and government driving records for 2 years after licensing. The DSBT group recorded lower infraction rates and, controlling for vehicle ownership and age, comparable crash rates. The lower infraction rates for males, despite the higher vehicle ownership normally associated with greater and riskier driving exposure, are a positive and unexpected finding. Crashes are multifactorial events less obviously related to drivers’ skills or intentions, and the comparable crash rates potentially indicate absence of overconfidence attributable to a form of advanced driver training. Overall, these results show that the substitution of relatively few hours of DSBT for on-road training is associated with reduced infractions and has no apparent influence on crashes in the first 2 years of unsupervised driving after licensing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".